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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2309.12553 (eess)
[Submitted on 22 Sep 2023]

Title:ICASSP 2023 Acoustic Echo Cancellation Challenge

Authors:Ross Cutler, Ando Saabas, Tanel Parnamaa, Marju Purin, Evgenii Indenbom, Nicolae-Catalin Ristea, Jegor Gužvin, Hannes Gamper, Sebastian Braun, Robert Aichner
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Abstract:The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20ms, as well as including a full-band version of AECMOS. We open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an objective metric for researchers to quickly test their results. The winners of this challenge were selected based on the average mean opinion score (MOS) achieved across all scenarios and the word accuracy (WAcc) rate.
Comments: arXiv admin note: substantial text overlap with arXiv:2202.13290, arXiv:2009.04972
Subjects: Audio and Speech Processing (eess.AS); Sound (cs.SD)
Cite as: arXiv:2309.12553 [eess.AS]
  (or arXiv:2309.12553v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2309.12553

Submission history

From: Ross Cutler [view email]
[v1] Fri, 22 Sep 2023 00:51:19 UTC (1,198 KB)
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